In practice, it can be extremely difficult to determine the prevailing quality of the prevailing technical work result of a technical installation, in particular, a production plant. In contrast to determining physical quantities using measuring techniques, in many cases there are no common direct measuring methods available for determining the quality parameters of production results, In some cases, there is success in assembling a highly specialized, complex measuring arrangement which, for instance, is based on radiological, electromagnetic or optical principles or a combination thereof. Many times, however, it is still necessary for the prevailing quality parameters to be subjectively determined by experienced operating personnel, for example, within the framework of a "quality control."
This produces a multitude of disadvantages. First of all, the determination of quality parameters by experienced operating personnel is neither representative nor reproducible. Rather, assessments of this kind vary even in the short term, depending on the operating personnel employed and their respective daily condition. Furthermore, operating personnel can generally only carry out evaluations of quality parameters on selected production results of the respective installation by taking random samples. A temporary absence or a change of "experienced operating personnel", for example, make it impossible to prevent unreproducible assessment variations in the long term, as well.
Secondly, exceptional outlay is required to be able to use the quality parameters, gained from the assessments by the operating personnel, along the lines of open-loop or closed-loop control engineering in the form of control variables or adjusted setpoint values for influencing the operational performance of the respective technical installation. Particularly in the case of high-speed, possibly fully automatic production plants, it is almost impossible in practice for the characteristic quality values, gained from random samples, to be made usable sufficiently quickly to influence the operational equipment of the technical installation.
In an article entitled "Recent Developments and Trends In Quality Control Technology For Resistance Welding, " by K. Matsayuma, the possibility of continuously determining specific parameters for resistance welding during a welding described. Also mentioned in the possibility of determining the parameters with the aid of a neural network.
In another article entitled "An Intelligent Control System for Resistance Spot Welding Using A Neural Network And Fuzzy Logic" by R. W. Messler, an intelligent control system is described, which is based on fuzzy logic and is used for compensating variations and faults during the automatic resistance welding operation.